Adaptive compute size per workload

ABSTRACT

Methods and apparatus relating to techniques for power management. In an example, an apparatus comprises logic, at least partially comprising hardware logic, to receive one or more frames for a workload, determine one or more compute resource parameters for the workload, and store the one or more compute resource parameters for the workload in a memory in association with workload context data for the workload. Other embodiments are also disclosed and claimed.

FIELD

The present disclosure generally relates to the field of electronics.More particularly, some embodiments relate to techniques to implement anadaptive compute size on a per-workload basis in a data processingsystem, e.g., a graphics processor.

BACKGROUND

As integrated circuit fabrication technology improves, manufacturers areable to integrate additional functionality onto a single siliconsubstrate. As the number of the functions increases, so does the numberof components on a single Integrated Circuit (IC) chip. Additionalcomponents add additional signal switching, in turn, generating moreheat and/or consuming more power. The additional heat may damagecomponents on the chip by, for example, thermal expansion. Also, theadditional power consumption may limit usage locations and/or usagemodels for such devices, e.g., especially for devices that rely onbattery power to function. Hence, efficient power management can have adirect impact on efficiency, longevity, as well as usage models forelectronic devices.

Moreover, current parallel graphics data processing includes systems andmethods developed to perform specific operations on graphics data suchas, for example, linear interpolation, tessellation, rasterization,texture mapping, depth testing, etc. Traditionally, graphics processorsused fixed function computational units to process graphics data;however, more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt, CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentembodiments can be understood in detail, a more particular descriptionof the embodiments, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments and are therefore not to be considered limiting ofits scope.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein.

FIG. 2A-2D illustrate a parallel processor components, according to anembodiment.

FIGS. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments.

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs are communicatively coupled to a plurality of multi-coreprocessors.

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment.

FIGS. 6 and 7C are schematic illustrations of architectures to implementan adaptive compute size per workload, according to embodiments.

FIGS. 7A-7B are flowcharts illustrating operations in a method toimplement an adaptive compute size per workload, according toembodiments.

FIG. 8 illustrates a block diagram of a switching regulator according toan embodiment.

FIG. 9 is a block diagram of a system including a streamingmultiprocessor, in accordance with one or more embodiments.

FIG. 10 illustrates a block diagram of a parallel processing system,according to one embodiment.

FIG. 11 is a block diagram of a processing system, according to anembodiment.

FIG. 12 is a block diagram of a processor according to an embodiment.

FIG. 13 is a block diagram of a graphics processor, according to anembodiment.

FIG. 14 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments.

FIG. 15 is a block diagram of a graphics processor provided by anadditional embodiment.

FIG. 16 illustrates thread execution logic including an array ofprocessing elements employed in some embodiments.

FIG. 17 is a block diagram illustrating a graphics processor instructionformats according to some embodiments.

FIG. 18 is a block diagram of a graphics processor according to anotherembodiment.

FIG. 19A-19B illustrate a graphics processor command format and commandsequence, according to some embodiments.

FIG. 20 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments.

FIG. 21 is a block diagram illustrating an IP core development system,according to an embodiment.

FIG. 22 is a block diagram illustrating an exemplary system on a chipintegrated circuit, according to an embodiment.

FIG. 23 is a block diagram illustrating an additional exemplary graphicsprocessor.

FIG. 24 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit, according to anembodiment.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of various embodiments.However, various embodiments may be practiced without the specificdetails. In other instances, well-known methods, procedures, components,and circuits have not been described in detail so as not to obscure theparticular embodiments. Further, various aspects of embodiments may beperformed using various means, such as integrated semiconductor circuits(“hardware”), computer-readable instructions organized into one or moreprograms (“software”), or some combination of hardware and software. Forthe purposes of this disclosure reference to “logic” shall mean eitherhardware, software, firmware, or some combination thereof.

Some embodiments discussed herein may be applied in any processor (suchas GPCPU, CPU, GPU, etc.), graphics controllers, etc. Other embodimentsare also disclosed and claimed. Further, some embodiments may be appliedin computing systems that include one or more processors (e.g., with oneor more processor cores), such as those discussed herein, including forexample mobile computing devices, e.g., a smartphone, tablet, UMPC(Ultra-Mobile Personal Computer), laptop computer, Ultrabook™ computingdevice, wearable devices (such as a smart watch or smart glasses), etc.

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent toone of skill in the art that the embodiments described herein may bepracticed without one or more of these specific details. In otherinstances, well-known features have not been described to avoidobscuring the details of the present embodiments.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that an include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O Hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s), 112 memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment at least a portion of the components ofthe computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation the number of partition units 220A-220N is configuredto be equal to the number of memory units, such that a first partitionunit 220A has a corresponding first memory unit 224A, a second partitionunit 220B has a corresponding memory unit 224B, and an Nth partitionunit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment the memory crossbar 216 has aconnection to the memory interface 218 to communicate with the I/O unit204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample and in one embodiment, some instances of the parallel processingunit 202 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Dirty updates can also be sent to theframe buffer via the frame buffer interface 225 for opportunisticprocessing. In one embodiment the frame buffer interface 225 interfaceswith one of the memory units in parallel processor memory, such as thememory units 224A-224N of FIG. 2 (e.g., within parallel processor memory222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments the ROP 226 includes compression logic tocompress z or color data that is written to memory and decompress z orcolor data that is read from memory. In some embodiments, the ROP 226 isincluded within each processing cluster (e.g., cluster 214A-214N of FIG.2) instead of within the partition unit 220. In such embodiment, readand write requests for pixel data are transmitted over the memorycrossbar 216 instead of pixel fragment data. The processed graphics datamay be displayed on a display device, such as one of the one or moredisplay device(s) 110 of FIG. 1, routed for further processing by theprocessor(s) 102, or routed for further processing by one of theprocessing entities within the parallel processor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2 and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed vis the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 308) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 308.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile (talk more about tiling) andoptionally a cache line index. The MMU 245 may include addresstranslation lookaside buffers (TLB) or caches that may reside within thegraphics multiprocessor 234 or the L1 cache or processing cluster 214.The physical address is processed to distribute surface data accesslocality to allow efficient request interleaving among partition units.The cache line index may be used to determine whether a request for acache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 324. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 324. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 324.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 324. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 324 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 324to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIGS. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346. Thevarious components can communicate via an interconnect fabric 327. Inone embodiment the interconnect fabric 327 includes one or more crossbarswitches to enable communication between the various components of thegraphics multiprocessor 325.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440-443 (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440-443 support a communication throughput of 4 GB/s, 30 GB/s, 80GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 444-445, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440-443. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 433 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects 430-431,respectively, and each GPU 410-413 is communicatively coupled to GPUmemory 420-423 over GPU memory interconnects 450-453, respectively. Thememory interconnects 430-431 and 450-453 may utilize the same ordifferent memory access technologies. By way of example, and notlimitation, the processor memories 401-402 and GPU memories 420-423 maybe volatile memories such as dynamic random access memories (DRAMs)(including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5,GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatilememories such as 3D XPoint or Nano-Ram. In one embodiment, some portionof the memories may be volatile memory and another portion may benon-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 426may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, N is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, N (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 440, biasingtechniques are used to ensure that the data stored in graphics memories433-434, M is data which will be used most frequently by the graphicsprocessing engines 431-432, N and preferably not used by the cores460A-460D (at least not frequently). Similarly, the biasing mechanismattempts to keep data needed by the cores (and preferably not thegraphics processing engines 431-432, N) within the caches 462A-462D, 456of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 462 and caches462A-462D, 426.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 446 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 3) may be configured to perform the functionsof one or more of a vertex processing unit 504, a tessellation controlprocessing unit 508, a tessellation evaluation processing unit 512, ageometry processing unit 516, and a fragment/pixel processing unit 524.The functions of data assembler 502, primitive assemblers 506, 514, 518,tessellation unit 510, rasterizer 522, and raster operations unit 526may also be performed by other processing engines within a processingcluster (e.g., processing cluster 214 of FIG. 3) and a correspondingpartition unit (e.g., partition unit 220A-220N of FIG. 2). The graphicsprocessing pipeline 500 may also be implemented using dedicatedprocessing units for one or more functions. In one embodiment, one ormore portions of the graphics processing pipeline 500 can be performedby parallel processing logic within a general purpose processor (e.g.,CPU). In one embodiment, one or more portions of the graphics processingpipeline 500 can access on-chip memory (e.g., parallel processor memory222 as in FIG. 2) via a memory interface 528, which may be an instanceof the memory interface 218 of FIG. 2.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 50. The primitive assembler 506 readingsstored vertex attributes as needed and constructs graphics primitivesfor processing by tessellation control processing unit 508. The graphicsprimitives include triangles, line segments, points, patches, and soforth, as supported by various graphics processing applicationprogramming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2,and/or system memory 104 as in FIG. 1, to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

As described above, many existing graphics systems maintain onlyarchitecture state data for use in scheduling resources for a workload,which may limit a system's ability to dynamically adjust computeresource sizing for a given workload. To address these and other issues,the techniques described herein enable a processing system, e.g., agraphics system, to implement adaptive compute resource sizing on aper-workload basis.

FIG. 6 is a block diagram of an architecture in which techniques toimplement efficient multi-context thread distribution may beimplemented, according to examples. Referring to FIG. 6, in someexamples a graphics processing unit 600 architecture 610 coupled to aplurality of streaming microprocessors (SMs) SM0, SM1, up to SM Nindicated by reference numeral 620A, 620B, up to 620N. The streamingmicroprocessors (SMs) may be referred to collectively by referencenumeral 620.

The streaming microprocessors (SMs) 620 comprise a plurality ofexecution units (EUs), also referred to as or cores, identified byreference numeral 622. In the example depicted in FIG. 6 each streamingmicroprocessor (SM) comprises six EUs, but it will be appreciated thateach streaming microprocessor (SM) 620 may comprise more or fewer EUs622. Further, each streaming microprocessor (SM) 620 may comprise atleast one cache 624.

FIGS. 7A and 7B are flowcharts illustrating operations in a method toimplement an adaptive compute size on a per-workload basis in a dataprocessing system, and FIG. 7C is a schematic block diagram of data setsin a method to implement an adaptive compute size on a per-workloadbasis in a data processing system, according to embodiments. In someexamples the operations depicted in FIG. 7A-7B may be implemented aslogic executable in one or more controllers. In various examples thelogic may be implemented as logic instructions stored in a memory andexecutable on a processor (i.e., software). In other examples the logicmay be reduced to programmable circuitry such as in a field programmablegate array (FPGA) or reduce to fixed circuitry in hardware, orcombinations thereof.

Referring to FIGS. 7A-7C, in at operation 710 one or more frames arereceived for a workload. For example, one or more frames may be receivedin a GPU 600 for processing and assigned to one or more SMs 620. Atoperation 715 compute resource parameters for the workload aredetermined. In some examples logic executing on the GPU 600 maydetermine a number of execution units 622 to be assigned to theworkload. Alternatively, or in addition, the logic may determine atleast one of an operating status (e.g., idle, active) of one or more ofthe execution units assigned to the workload of one or more of theexecution units assigned to the workload.

At operation 720 the compute resource parameters determined in operation715 are stored in association with context data for the workload.Referring briefly to FIG. 7C, in some examples compute resourceparameters from various EUs 760 are stored in association with workloadcontext data in a workload context memory 765. In some examples theworkload context memory 765 may be implemented in a local cache, e.g., acache 624 on a SM 620.

The compute resource parameters stored in workload may be used toschedule resources for subsequent operations for the workload. Referringto FIG. 7B, at operation 730, one or more frames are received for theworkload. For example, one or more subsequent frames may be received inthe GPU 600 for processing. At operation 735 compute resource parametersfor the workload determined in operation 715 and stored in memory inoperation 720 are retrieved from the workload context memory 765.

At operation 740 the compute resource parameters retrieved from workloadcontext memory are used to power up and schedule compute resources forthe frame(s) received in operation 730. Referring briefly to FIG. 7C, insome examples compute resource parameters retrieved from workloadcontext memory 765 are forwarded to a scheduler 770, which may use thecompute resource parameters to schedule compute resources for theframe(s) received in operation 730.

At operation 745 the compute resource parameters stored in memory may beupdated. For example, the compute resource parameters in workloadcontext memory 765 may be updated to reflect the current status of thecompute resource parameters assigned in operation 740.

The operations depicted in FIG. 8 may be repeated for each new frame(s)received for processing. In this manner the compute resource parametersstored in the workload context memory 765 may be modified dynamically toreflect current resource allocation conditions for the workload. Whenthe workload is finished the compute resource parameters in workloadcontext memory 765 may be deleted.

Thus, the subject matter described herein allows hardware in a graphicsprocessing system to self-tune resource allocation on a per-workloadbasis. For example, if a particular workload reflects increasingresource consumption over a period of time then a scheduler maygradually increase the amount of compute resources dedicated to incomingframe(s) on a real-time basis. The scheduler may monitor one or moreperformance parameters of the frame(s) processed by the graphicsprocessing system. If the performance parameters indicate that theresources dedicated to the frame(s) are excessive, then the schedulermay reduce the resources directed to the next frame(s) for the workload.By contrast, if the performance parameters indicate that the resourcesdedicated to the frame(s) are insufficient, then the scheduler mayincrease the resources directed to the next frame(s) for the workload.

Power Components

FIG. 8 illustrates a block diagram of a switching regulator according toan embodiment. One or more switching regulators shown in FIG. 8 may beincorporated in various systems discussed herein to provide power to oneor more Integrated Circuit (IC) chips. While a single phase of thecurrent-parking switching regulator with a single inductor may bediscussed with reference to FIG. 8, one or more of the multiple phasesof the current-parking switching regulator may be implemented with asplit inductor. Furthermore, a combination of one or morecurrent-parking switching regulators (with or without a split inductor)may be used with one or more conventional electric power conversiondevices to provide power to the load (e.g., logic circuitry 814).

More particularly, FIG. 8 illustrates a system 800 that includes aswitching regulator (sometimes referred to as a current-parkingswitching regulator). The current-parking switching regulator may be amulti-phase switching regulator in various embodiments. The multi-phasecontrol unit 802 is coupled to multiple phases, where each phase mayinclude one or more upstream phases 804 and one or more downstreamphases 806. As shown, an electrical power source 808 is coupled toupstream control logic 810 (which provides a current control mechanismsin each upstream phase). More than one upstream control logic may beused in various implementations. Each upstream phase may include aninductor (not shown) that is coupled to a respective downstream phase.In an embodiment, the upstream phases may each include one or moreinductors. The multi-phase control unit 802 may configure any activeupstream control logic 810, e.g., to generate a current through aninductor coupled between the upstream phases and the downstream phases.The downstream control logic 812 may be configured by the multi-phasecontrol unit 802 to be ON, OFF, or switching to regulate the voltagelevel at the load (e.g., logic circuitry 814). In turn, the downstreamcontrol logic 812 may be configured by the multi-phase control unit 802to maintain the voltage level at the load within a range based at leastin part on Vmin (minimum voltage) and Vmax (maximum voltage) values.

In one embodiment, an inductor (coupled between a downstream phase and arespective upstream phase) may be positioned outside of a semiconductorpackage 816 that includes the load 814. Another inductor (not shown) maybe positioned inside of the package 816, e.g., to reduce parasiticcapacitance. In one embodiment, the inductor inside the package 816 maybe a planar air-core inductor that is coupled to the logic circuitry 814via one or more switching logic which include planar Metal-OxideSemiconductor Field-Effect Transistors (MOSFETs). Furthermore, one ormore of the components discussed herein (e.g., with reference to FIGS.8m 9, and/or 10, including, for example, L3 cache, upstream controllogic, and/or downstream control logic) may be provided in substratelayer(s) (e.g., between semiconductor packages), on an integratedcircuit die, or outside of a semiconductor package (e.g., on a PrintedCircuit Board (PCB)) in various embodiments.

FIG. 9 is a block diagram of a system 900 including a streamingmultiprocessor 902, in accordance with one or more embodiments. Thestreaming multiprocessor may include 32 Single-Instruction, MultipleThread (SIMT) lanes 904 that are capable of collectively issuing up to32 instructions per clock cycle, e.g., one from each of 32 threads. Moreor less lanes may be present depending on the implementation such as 64,128, 256, etc. The SIMT lanes 904 may in turn include one or more:Arithmetic Logic Units (ALUs) 906, Special Function Units (SFUs) 908,memory units (MEM) 910, and/or texture units (TEX) 912.

In some embodiments, one or more of ALU(s) 906 and/or TEX unit(s) 912may be low energy or high capacity, e.g., such as discussed withreference to items 920 and 922. For example, the system may map 100% ofthe register addresses for threads 0-30 to the low energy portion and100% of the register addresses for threads 31-127 to the high capacityportion. As another example, the system may map 20% of each thread'sregisters to the low energy portion and to map 80% of each thread'sregisters to the high capacity portion. Moreover, the system maydetermine the number of entries allocated per thread based on runtimeinformation.

As illustrated in FIG. 9, the streaming multiprocessor 902 also includea register file 914, a scheduler logic 916 (e.g., for scheduling threadsor thread groups, or both), and shared memory 918, e.g., local scratchstorage. As discussed herein, a “thread group” refers to a plurality ofthreads that are grouped with ordered (e.g., sequential or consecutive)thread indexes. Generally, a register file refers to an array ofregisters accessed by components of a processor (including a graphicsprocessor) such as those discussed herein. The register file 914includes a low energy portion or structure 920 and a high capacityportion or structure 922. The streaming multiprocessor 902 may beconfigured to address the register file 914 using a single logicalnamespace for both the low energy portion and the high capacity portion.

In some embodiments, the system may include a number of physicalregisters which can be shared by the simultaneously running threads onthe system. This allows the system to use a single namespace toimplement a flexible register mapping scheme. A compiler may thenallocate register live ranges to register addresses, and the compilermay use a register allocation mechanism to minimize or reduce the numberof registers used per thread. Multiple live ranges can be allocated tothe same register address as long as the live ranges do not overlap inan embodiment. This allows for determination, e.g., at runtime and afterinstructions have been compiled, of how many entries per thread will beallocated in the low energy portion versus the high capacity portion.For example, the system may map 100% of the register addresses forthreads 0-30 to the low energy portion and 100% of the registeraddresses for threads 31-127 to the high capacity portion. As anotherexample, the system may map 20% of each thread's registers to the lowenergy portion and to map 80% of each thread's registers to the highcapacity portion. The system may determine the number of entriesallocated per thread based on runtime information, e.g., regarding thenumber of thread groups executing and the marginal benefit fromlaunching more thread groups or allocating a smaller number of threadgroups more space in the low energy portion.

FIG. 10 illustrates a block diagram of a parallel processing system1000, according to one embodiment. System 1000 includes a ParallelProcessing (Previously Presented) subsystem 1002 which in turn includesone or more Parallel Processing Units (PPUs) PPU-0 through PPU-P. EachPPU is coupled to a local Parallel Processing (PP) memory (e.g., Mem-0through MEM-P, respectively). In some embodiments, the PP subsystemsystem 1002 may include P number of PPUs. PPU-0 1004 and parallelprocessing memories 1006 may be implemented using one or more integratedcircuit devices, such as programmable processors, Application SpecificIntegrated Circuits (ASICs), or memory devices.

Referring to FIG. 10. several optional switch or connections 1007 areshown that may be used in system 1000 to manage power. While severalswitches 1007 are shown, embodiments are not limited to the specificallyshown switches and more or less switches may be utilized depending onthe implementation. These connections/switches 1007 may be utilized forclock gating or general power gating. Hence, items 1007 may include oneor more of a power transistor, on-die switch, power plane connections,or the like. In an embodiment, prior to shutting power to a portion ofsystem 1000 via switches/connections 1007, logic (e.g., amicrocontroller, digital signal processor, firmware, etc.) may ensurethe results of operation are committed (e.g., to memory) or finalized tomaintain correctness.

Further, in some embodiments, one or more of PPUs in parallel processingsubsystem 1002 are graphics processors with rendering pipelines that maybe configured to perform various tasks such as those discussed hereinwith respect to other figures. The graphics information/data may becommunicated via memory bridge 1008 with other components of a computingsystem (including components of system 1000). The data may becommunicated via a shared bus and/or one or more interconnect(s) 1010(including, for example, one or more direct or point-to-point links).PPU-0 1004 may access its local parallel processing memory 1014 (whichmay be used as graphics memory including, e.g., a frame buffer) to storeand update pixel data, delivering pixel data to a display device (suchas those discussed herein), etc. In some embodiments, the parallelprocessing subsystem 1002 may include one or more PPUs that operate asgraphics processors and one or more other PPUs that operate to performgeneral-purpose computations. The PPUs may be identical or different,and each PPU may have access to its own dedicated parallel processingmemory device(s), no dedicated parallel processing memory device(s), ora shared memory device or cache.

In an embodiment, operations performed by PPUs may be controlled byanother processor (or one of the PPUs) generally referred to as a masterprocessor or processor core. In one embodiment, the masterprocessor/core may write a stream of commands for each PPU to a pushbuffer in various locations such as a main system memory, a cache, orother memory such as those discussed herein with reference to otherfigures. The written commands may then be read by each PPU and executedasynchronously relative to the operation of master processor/core.

Furthermore, as shown in FIG. 10, PPU-0 includes a front end logic 1020which may include an Input/Output (I/O or IO) unit (e.g., to communicatewith other components of system 1000 through the memory bridge 1008)and/or a host interface (e.g., which receives commands related toprocessing tasks). The front end 1020 may receive commands read by thehost interface (for example from the push buffer)). The front end 1020in turn provides the commands to a work scheduling unit 1022 thatschedules and allocates operation(s)/task(s) associated with thecommands to a processing cluster array or arithmetic subsystem 1024 forexecution.

As shown in FIG. 10, the processing cluster array 1024 may include oneor more General Processing Cluster (GPC) units (e.g., GPC-0 1026, GPC-11028, through GPC-M 1030). Each GPC may be capable of executing a largenumber (e.g., hundreds or thousands) of threads concurrently, where eachthread is an instance of a program. In various applications, differentGPCs may be allocated for processing different types of programs or forperforming different types of computations. For example, in a graphicsapplication, a first set of GPCs (e.g., including one or more GPC units)may be allocated to perform tessellation operations and to produceprimitive topologies for patches, and a second set of GPCs (e.g.,including one or more GPC units) may be allocated to performtessellation shading to evaluate patch parameters for the primitivetopologies and to determine vertex positions and other per-vertexattributes. The allocation of GPCs may vary depending on the workloadarising for each type of program or computation.

Additionally, processing tasks that are assigned by the work schedulingunit 1022 may include indices of data to be processed, suchsurface/patch data, primitive data, vertex data, pixel data, and/orstate parameters and commands defining how the data is to be processed(e.g., what program is to be executed). The work scheduling unit 1022may be configured to fetch the indices corresponding to the tasks, ormay receive the indices from front end 1020. Front end 1020 may alsoensure that GPCs are configured to a valid state before the processingspecified by the push buffers is initiated.

In one embodiment, the communication path 1012 is a Peripheral ComponentInterface (PCI) express (or PCI-e) link, in which dedicated lanes may beallocated to each PPU. Other communication paths may also be used. Forexample, commands related to processing tasks may be directed to thehost interface 1018, while commands related to memory operations (e.g.,reading from or writing to parallel processing memory 1014) may bedirected to a memory crossbar unit 1032.

In some embodiments, parallel processing subsystem 1002 may beimplemented as an add-in card that is inserted into an expansion slot ofcomputer system or server (such as a blade server). In otherembodiments, a PPU may be integrated on a single chip with a bus bridge,such as memory bridge 1008, an I/O bridge, etc. In still otherembodiments, some or all components of PPU may be integrated on a singleintegrated circuit chip with one or more other processor cores, memorydevices, caches, etc.

Furthermore, one of the major problems with today's modern processors isthey have hit a clock rate limit at around 4 GHz. At this point theyjust generate too much heat for the current technology and requirespecial and expensive cooling solutions. This is because as we increasethe clock rate, the power consumption rises. In fact, the powerconsumption of a CPU, if you fix the voltage, is approximately the cubeof its clock rate. To make this worse, as you increase the heatgenerated by the CPU, for the same clock rate, the power consumptionalso increases due to the properties of the silicon. This conversion ofpower into heat is a complete waste of energy. This increasinglyinefficient use of power eventually means you are unable to either poweror cool the processor sufficiently and you reach the thermal limits ofthe device or its housing, the so-called power wall.

Faced with not being able to increase the clock rate, makingforever-faster processors, the processor manufacturers had to come upwith another game plan. They have been forced down the route of addingmore cores to processors, rather than continuously trying to increaseCPU clock rates and/or extract more instructions per clock throughinstruction-level parallelism.

Moreover, power usage is a big consideration when designing machinesthat constantly run. Often the operating costs of running asupercomputer over just a few years can equate to the cost of installingit in the first place. Certainly, the cost of running such a machineover its lifetime will easily exceed the original installation costs.Power usage comes from the components themselves, but also from thecooling necessary to allow such computers to operate. Even one high-endworkstation with four GPUs requires some planning on how to keep itcool. Unless you live in a cold climate and can banish the computer tosomewhere cold, it will do a nice job of heating up the office for you.Put a number of such machines into one room, and very rapidly the airtemperature in that room will start to rise to quite unacceptablelevels.

A significant amount of power is therefore expended on installing airconditioning systems to ensure computers remain cool and can operatewithout producing errors. This is especially so where summertemperatures can reach 85 F/30 C or higher. Air conditioning isexpensive to run. Significant thought should be given to how best tocool such a system and if the heat energy can in some way be reused.Liquid-cooled systems are very efficient in this way in that the liquidcan be circulated through a heat exchanger and into a conventionalheating system without any chance of the two liquids ever mixing. Withthe ever-increasing costs of natural resources, and the increasingpressures on companies to be seen as green, simply pumping the heat outthe window is no longer economically or socially acceptable.

Liquid-cooled systems provide an interesting option in terms ofrecycling the waste heat energy. While an air-cooled system can only beused to heat the immediate area it is located in, heat from liquid-basedcoolants can be pumped elsewhere. By using a heat exchanger, the coolantcan be cooled using conventional water. This can then be pumped into aheating system or even used to heat an outdoor swimming pool or otherlarge body of water. Where a number of such systems are installed, suchas in a company or university computer center, it can really make senseto use this waste heat energy to reduce the heating bill elsewhere inthe organization.

Many supercomputer installations site themselves next to a major riverprecisely because they need a ready supply of cold water. Others uselarge cooling towers to dissipate the waste heat energy. Neithersolution is particularly green. Having paid for the energy already itmakes little sense to simply throw it away when it could so easily beused for heating. When considering power usage, we must also rememberthat program design actually plays a very big role in power consumption.The most expensive operation, power wise, is moving data on and offchip. Thus, simply making efficient use of the registers and sharedmemory within the device vastly reduces power usage. If you alsoconsider that the total execution time for well-written programs is muchsmaller than for poorly written ones, you can see that rewriting oldprograms to make use of new features such as larger shared memory caneven reduce operating costs in a large data center.

Referring to FIG. 10, memory interface 1014 includes N partition units(e.g., Unit-0 1034, Unit-1 1036, through Unit-N 10-38) that are eachdirectly coupled to a corresponding portion of parallel processingmemory 1006 (such as Mem-0 1040, Mem-1 1042, through Mem-N 1044). Thenumber of partition units may generally be equal to the number ofPreviously Presented memory (or N as shown). The Previously Presentedmemory may be implemented with volatile memory such as Dynamic RandomAccess Memory (DRAM) or other types of volatile memory such as thosediscussed herein. In other embodiments, the number of partition unitsmay not equal the number of memory devices. Graphics data (such asrender targets, frame buffers, or texture maps) may be stored acrossPreviously Presented memory devices, allowing partition units to writeportions of graphics data in parallel to efficiently use the availablebandwidth of the parallel processing memory 1006.

Furthermore, any one of GPCs may process data to be written to any ofthe partition units within the parallel processing memory. Crossbar unit1032 may be implemented as an interconnect that is configured to routethe output of each GPC to the input of any partition unit or to anotherGPC for further processing. Hence, GPCs 1026 to 1030 may communicatewith memory interface 1014 through crossbar unit 1032 to read from orwrite to various other (or external) memory devices. As shown, crossbarunit 1032 may directly communicate with the front end 1020, as well ashaving a coupling (direct or indirect) to local memory 1006, to allowthe processing cores within the different GPCs to communicate withsystem memory and/or other memory that is not local to PPU. Furthermore,the crossbar unit 1032 may utilize virtual channels to organize trafficstreams between the GPCs and partition units.

System Overview

FIG. 11 is a block diagram of a processing system 1100, according to anembodiment. In various embodiments the system 1100 includes one or moreprocessors 1102 and one or more graphics processors 1108, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 1102 or processorcores 1107. In one embodiment, the system 1100 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 1100 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 1100 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1100 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 1100 is a television or set topbox device having one or more processors 1102 and a graphical interfacegenerated by one or more graphics processors 1108.

In some embodiments, the one or more processors 1102 each include one ormore processor cores 1107 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1107 is configured to process aspecific instruction set 1109. In some embodiments, instruction set 1109may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1107 may each processa different instruction set 1109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1107may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 1102 includes cache memory 1104.Depending on the architecture, the processor 1102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1102. In some embodiments, the processor 1102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1107 using knowncache coherency techniques. A register file 1106 is additionallyincluded in processor 1102 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1102.

In some embodiments, processor 1102 is coupled with a processor bus 1110to transmit communication signals such as address, data, or controlsignals between processor 1102 and other components in system 1100. Inone embodiment the system 1100 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1116 and an Input Output(I/O) controller hub 1130. A memory controller hub 1116 facilitatescommunication between a memory device and other components of system1100, while an I/O Controller Hub (ICH) 1130 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1116 is integrated within the processor.

Memory device 1120 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 1120 can operate as system memory for the system 1100, to storedata 1122 and instructions 1121 for use when the one or more processors1102 executes an application or process. Memory controller hub 1116 alsocouples with an optional external graphics processor 1112, which maycommunicate with the one or more graphics processors 1108 in processors1102 to perform graphics and media operations.

In some embodiments, ICH 1130 enables peripherals to connect to memorydevice 1120 and processor 1102 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1146, afirmware interface 1128, a wireless transceiver 1126 (e.g., Wi-Fi,Bluetooth), a data storage device 1124 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1140 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1142 connect input devices, suchas keyboard and mouse 1144 combinations. A network controller 1134 mayalso couple with ICH 1130. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 1110. It willbe appreciated that the system 1100 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 1130may be integrated within the one or more processor 1102, or the memorycontroller hub 1116 and I/O controller hub 1130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1112.

FIG. 12 is a block diagram of an embodiment of a processor 1200 havingone or more processor cores 1202A-1202N, an integrated memory controller1214, and an integrated graphics processor 1208. Those elements of FIG.12 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor1200 can include additional cores up to and including additional core1202N represented by the dashed lined boxes. Each of processor cores1202A-1202N includes one or more internal cache units 1204A-1204N. Insome embodiments each processor core also has access to one or moreshared cached units 1206.

The internal cache units 1204A-1204N and shared cache units 1206represent a cache memory hierarchy within the processor 1200. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 1206 and1204A-1204N.

In some embodiments, processor 1200 may also include a set of one ormore bus controller units 1216 and a system agent core 1210. The one ormore bus controller units 1216 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 1210 provides management functionality forthe various processor components. In some embodiments, system agent core1210 includes one or more integrated memory controllers 1214 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 1202A-1202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 1210 includes components for coordinating andoperating cores 1202A-1202N during multi-threaded processing. Systemagent core 1210 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 1202A-1202N and graphics processor 1208.

In some embodiments, processor 1200 additionally includes graphicsprocessor 1208 to execute graphics processing operations. In someembodiments, the graphics processor 1208 couples with the set of sharedcache units 1206, and the system agent core 1210, including the one ormore integrated memory controllers 1214. In some embodiments, a displaycontroller 1211 is coupled with the graphics processor 1208 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 1211 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 1208 or system agent core 1210.

In some embodiments, a ring based interconnect unit 1212 is used tocouple the internal components of the processor 1200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 1208 couples with the ring interconnect 1212 via an I/O link1213.

The exemplary I/O link 1213 represents at least one of multiplevarieties of I/O interconnects, including an on package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 1218, such as an eDRAM module.In some embodiments, each of the processor cores 1202A-1202N andgraphics processor 1208 use embedded memory modules 1218 as a sharedLast Level Cache.

In some embodiments, processor cores 1202A-1202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 1202A-1202N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 1202A-1202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 1202A-1202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor1200 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 13 is a block diagram of a graphics processor 1300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 1300 includesa memory interface 1314 to access memory. Memory interface 1314 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 1300 also includes a displaycontroller 1302 to drive display output data to a display device 1320.Display controller 1302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 1300includes a video codec engine 1306 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 1300 includes a block imagetransfer (BLIT) engine 1304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 1310. In someembodiments, GPE 1310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 1312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 1312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 1315.While 3D pipeline 1312 can be used to perform media operations, anembodiment of GPE 1310 also includes a media pipeline 1316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 1316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 1306. In some embodiments, media pipeline 1316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 1315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 1315.

In some embodiments, 3D/Media subsystem 1315 includes logic forexecuting threads spawned by 3D pipeline 1312 and media pipeline 1316.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 1315, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 1315 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Graphics Processing Engine

FIG. 14 is a block diagram of a graphics processing engine 1410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 1410 is a version ofthe GPE 1310 shown in FIG. 13. Elements of FIG. 14 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 1312 and media pipeline 1316 of FIG. 13 are illustrated. Themedia pipeline 1316 is optional in some embodiments of the GPE 1410 andmay not be explicitly included within the GPE 1410. For example and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 1410.

In some embodiments, GPE 1410 couples with or includes a commandstreamer 1403, which provides a command stream to the 3D pipeline 1312and/or media pipelines 1316. In some embodiments, command streamer 1403is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 1403 receives commands from the memory and sends thecommands to 3D pipeline 1312 and/or media pipeline 1316. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 1312 and media pipeline 1316. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 1312 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 1312 and/or image data andmemory objects for the media pipeline 1316. The 3D pipeline 1312 andmedia pipeline 1316 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 1414.

In various embodiments the 3D pipeline 1312 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 1414. The graphics core array 1414 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphic core array 1414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 1414 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 107 of FIG. 1 or core 1202A-1202N as in FIG. 12.

Output data generated by threads executing on the graphics core array1414 can output data to memory in a unified return buffer (URB) 1418.The URB 1418 can store data for multiple threads. In some embodimentsthe URB 1418 may be used to send data between different threadsexecuting on the graphics core array 1414. In some embodiments the URB1418 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 1420.

In some embodiments, graphics core array 1414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 1410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 1414 couples with shared function logic 1420that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 1420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 1414. In variousembodiments, shared function logic 1420 includes but is not limited tosampler 1421, math 1422, and inter-thread communication (ITC) 1423logic. Additionally, some embodiments implement one or more cache(s)1425 within the shared function logic 1420. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 1414. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 1420 and shared amongthe execution resources within the graphics core array 1414. The preciseset of functions that are shared between the graphics core array 1414and included within the graphics core array 1414 varies betweenembodiments.

FIG. 15 is a block diagram of another embodiment of a graphics processor1500. Elements of FIG. 15 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 1500 includes a ringinterconnect 1502, a pipeline front-end 1504, a media engine 1537, andgraphics cores 1580A-1580N. In some embodiments, ring interconnect 1502couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 1500 receives batches ofcommands via ring interconnect 1502. The incoming commands areinterpreted by a command streamer 1503 in the pipeline front-end 1504.In some embodiments, graphics processor 1500 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 1580A-1580N. For 3D geometry processing commands,command streamer 1503 supplies commands to geometry pipeline 1536. Forat least some media processing commands, command streamer 1503 suppliesthe commands to a video front end 1534, which couples with a mediaengine 1537. In some embodiments, media engine 1537 includes a VideoQuality Engine (VQE) 1530 for video and image post-processing and amulti-format encode/decode (MFX) 1533 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 1536 and media engine 1537 each generate executionthreads for the thread execution resources provided by at least onegraphics core 1580A.

In some embodiments, graphics processor 1500 includes scalable threadexecution resources featuring modular cores 1580A-1580N (sometimesreferred to as core slices), each having multiple sub-cores 1550A-550N,1560A-1560N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 1500 can have any number of graphicscores 1580A through 1580N. In some embodiments, graphics processor 1500includes a graphics core 1580A having at least a first sub-core 1550Aand a second sub-core 1560A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 1550A).In some embodiments, graphics processor 1500 includes multiple graphicscores 1580A-1580N, each including a set of first sub-cores 1550A-1550Nand a set of second sub-cores 1560A-1560N. Each sub-core in the set offirst sub-cores 1550A-1550N includes at least a first set of executionunits 1552A-1552N and media/texture samplers 1554A-1554N. Each sub-corein the set of second sub-cores 1560A-1560N includes at least a secondset of execution units 1562A-1562N and samplers 1564A-1564N. In someembodiments, each sub-core 1550A-1550N, 1560A-1560N shares a set ofshared resources 1570A-1570N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Execution Units

FIG. 16 illustrates thread execution logic 1600 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 16 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 1600 includes a shaderprocessor 1602, a thread dispatcher 1604, instruction cache 1606, ascalable execution unit array including a plurality of execution units1608A-1608N, a sampler 1610, a data cache 1612, and a data port 1614. Inone embodiment the scalable execution unit array can dynamically scaleby enabling or disabling one or more execution units (e.g., any ofexecution unit 1608A, 1608B, 1608C, 1608D, through 1608N-1 and 1608N)based on the computational requirements of a workload. In one embodimentthe included components are interconnected via an interconnect fabricthat links to each of the components. In some embodiments, threadexecution logic 1600 includes one or more connections to memory, such assystem memory or cache memory, through one or more of instruction cache1606, data port 1614, sampler 1610, and execution units 1608A-1608N. Insome embodiments, each execution unit (e.g. 1608A) is a stand-aloneprogrammable general purpose computational unit that is capable ofexecuting multiple simultaneous hardware threads while processingmultiple data elements in parallel for each thread. In variousembodiments, the array of execution units 1608A-1608N is scalable toinclude any number individual execution units.

In some embodiments, the execution units 1608A-1608N are primarily usedto execute shader programs. A shader processor 1602 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 1604. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units1608A-1608N. For example, the geometry pipeline (e.g., 1536 of FIG. 15)can dispatch vertex, tessellation, or geometry shaders to the threadexecution logic 1600 (FIG. 16) for processing. In some embodiments,thread dispatcher 1604 can also process runtime thread spawning requestsfrom the executing shader programs.

In some embodiments, the execution units 1608A-1608N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 1608A-1608N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units1608A-1608N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.

Each execution unit in execution units 1608A-1608N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 1608A-1608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 1606) are included in thethread execution logic 1600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,1612) are included to cache thread data during thread execution. In someembodiments, a sampler 1610 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 1610 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 1600 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor1602 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 1602 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 1602dispatches threads to an execution unit (e.g., 1608A) via threaddispatcher 1604. In some embodiments, pixel shader 1602 uses texturesampling logic in the sampler 1610 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 1614 provides a memory accessmechanism for the thread execution logic 1600 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 1614 includes or couples to one or more cachememories (e.g., data cache 1612) to cache data for memory access via thedata port.

FIG. 17 is a block diagram illustrating a graphics processor instructionformats 1700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 1700 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 1710. A 64-bitcompacted instruction format 1730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 1730. The native instructions availablein the 64-bit format 1730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 1713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format1710.

For each format, instruction opcode 1712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 1714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 1710 an exec-size field1716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 1716 is not available foruse in the 64-bit compact instruction format 1730.

Some execution unit instructions have up to three operands including twosource operands, src0 1720, src1 1722, and one destination 1718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 1724), where the instructionopcode 1712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 1710 includes anaccess/address mode field 1726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 1710 includes anaccess/address mode field 1726, which specifies an address mode and/oran access mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 1726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 1712bit-fields to simplify Opcode decode 1740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 1742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 1742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 1744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 1746 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 1748 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 1748 performs the arithmetic operations in parallelacross data channels. The vector math group 1750 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Graphics Pipeline

FIG. 18 is a block diagram of another embodiment of a graphics processor1800. Elements of FIG. 18 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 1800 includes a graphicspipeline 1820, a media pipeline 1830, a display engine 1840, threadexecution logic 1850, and a render output pipeline 1870. In someembodiments, graphics processor 1800 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 1800 via a ring interconnect 1802. In someembodiments, ring interconnect 1802 couples graphics processor 1800 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 1802 areinterpreted by a command streamer 1803, which supplies instructions toindividual components of graphics pipeline 1820 or media pipeline 1830.

In some embodiments, command streamer 1803 directs the operation of avertex fetcher 1805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 1803. In someembodiments, vertex fetcher 1805 provides vertex data to a vertex shader1807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 1805 andvertex shader 1807 execute vertex-processing instructions by dispatchingexecution threads to execution units 1852A-1852B via a thread dispatcher1831.

In some embodiments, execution units 1852A-1852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 1852A-1852B have anattached L1 cache 1851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 1820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 1813 operatesat the direction of hull shader 1811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 1820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 1811, tessellator 1813, and domain shader 1817) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 1819 via one or more threads dispatched to executionunits 1852A-1852B, or can proceed directly to the clipper 1829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader1819 receives input from the vertex shader 1807. In some embodiments,geometry shader 1819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 1829 processes vertex data. The clipper1829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 1873 in the render output pipeline1870 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 1850. In some embodiments, anapplication can bypass the rasterizer and depth test component 1873 andaccess un-rasterized vertex data via a stream out unit 1823.

The graphics processor 1800 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 1852A-1852B and associated cache(s) 1851,texture and media sampler 1854, and texture/sampler cache 1858interconnect via a data port 1856 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 1854, caches 1851, 1858 and execution units1852A-1852B each have separate memory access paths.

In some embodiments, render output pipeline 1870 contains a rasterizerand depth test component 1873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache1878 and depth cache 1879 are also available in some embodiments. Apixel operations component 1877 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 1841, or substituted at display time by the displaycontroller 1843 using overlay display planes. In some embodiments, ashared L3 cache 1875 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 1830 includes amedia engine 1837 and a video front end 1834. In some embodiments, videofront end 1834 receives pipeline commands from the command streamer1803. In some embodiments, media pipeline 1830 includes a separatecommand streamer. In some embodiments, video front-end 1834 processesmedia commands before sending the command to the media engine 1837. Insome embodiments, media engine 1837 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic1850 via thread dispatcher 1831.

In some embodiments, graphics processor 1800 includes a display engine1840. In some embodiments, display engine 1840 is external to processor1800 and couples with the graphics processor via the ring interconnect1802, or some other interconnect bus or fabric. In some embodiments,display engine 1840 includes a 2D engine 1841 and a display controller1843. In some embodiments, display engine 1840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 1843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 1820 and media pipeline 1830 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 19A is a block diagram illustrating a graphics processor commandformat 1900 according to some embodiments. FIG. 19B is a block diagramillustrating a graphics processor command sequence 1910 according to anembodiment. The solid lined boxes in FIG. 19A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 1900 of FIG. 19A includes data fields to identify atarget client 1902 of the command, a command operation code (opcode)1904, and the relevant data 1906 for the command. A sub-opcode 1905 anda command size 1908 are also included in some commands.

In some embodiments, client 1902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 1904 and, if present, sub-opcode 1905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 1906. For some commands an explicit commandsize 1908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 19B shows an exemplary graphics processorcommand sequence 1910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 1910 maybegin with a pipeline flush command 1912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 1922 and the media pipeline 1924 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 1912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 1913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 1913is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 1912 isrequired immediately before a pipeline switch via the pipeline selectcommand 1913.

In some embodiments, a pipeline control command 1914 configures agraphics pipeline for operation and is used to program the 3D pipeline1922 and the media pipeline 1924. In some embodiments, pipeline controlcommand 1914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 1914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 1916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 1916 includes selecting the size and number ofreturn buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 1920,the command sequence is tailored to the 3D pipeline 1922 beginning withthe 3D pipeline state 1930 or the media pipeline 1924 beginning at themedia pipeline state 1940.

The commands to configure the 3D pipeline state 1930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 1930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 1932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 1932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 1932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 1932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 1922 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 1922 is triggered via an execute 1934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 1910follows the media pipeline 1924 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 1924 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 1924 is configured in a similarmanner as the 3D pipeline 1922. A set of commands to configure the mediapipeline state 1940 are dispatched or placed into a command queue beforethe media object commands 1942. In some embodiments, media pipelinestate commands 1940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,media pipeline state commands 1940 also support the use of one or morepointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 1942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 1942. Once the pipeline state is configured andmedia object commands 1942 are queued, the media pipeline 1924 istriggered via an execute command 1944 or an equivalent execute event(e.g., register write). Output from media pipeline 1924 may then be postprocessed by operations provided by the 3D pipeline 1922 or the mediapipeline 1924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 20 illustrates exemplary graphics software architecture for a dataprocessing system 2000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application2010, an operating system 2020, and at least one processor 2030. In someembodiments, processor 2030 includes a graphics processor 2032 and oneor more general-purpose processor core(s) 2034. The graphics application2010 and operating system 2020 each execute in the system memory 2050 ofthe data processing system.

In some embodiments, 3D graphics application 2010 contains one or moreshader programs including shader instructions 2012. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 2014 in a machinelanguage suitable for execution by the general-purpose processor core2034. The application also includes graphics objects 2016 defined byvertex data.

In some embodiments, operating system 2020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 2020 can support agraphics API 2022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 2020uses a front-end shader compiler 2024 to compile any shader instructions2012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 2010. In some embodiments, the shader instructions 2012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 2026 contains a back-endshader compiler 2027 to convert the shader instructions 2012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 2012 in the GLSL high-level language are passed to a usermode graphics driver 2026 for compilation. In some embodiments, usermode graphics driver 2026 uses operating system kernel mode functions2028 to communicate with a kernel mode graphics driver 2029. In someembodiments, kernel mode graphics driver 2029 communicates with graphicsprocessor 2032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 21 is a block diagram illustrating an IP core development system2100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system2100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility2130 can generate a software simulation 2110 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation2110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 2112. The simulation model 2112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 2115 can then be created or synthesized from thesimulation model 2112. The RTL design 2115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 2115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 2115 or equivalent may be further synthesized by thedesign facility into a hardware model 2120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 2165 using non-volatile memory 2140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 2150 or wireless connection 2160. Thefabrication facility 2165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIGS. 22-24 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 22 is a block diagram illustrating an exemplary system on a chipintegrated circuit 2200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 2200includes one or more application processor(s) 2205 (e.g., CPUs), atleast one graphics processor 2210, and may additionally include an imageprocessor 2215 and/or a video processor 2220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 2200 includes peripheral or bus logic including a USBcontroller 2225, UART controller 2230, an SPI/SDIO controller 2235, andan I²S/I²C controller 2240. Additionally, the integrated circuit caninclude a display device 2245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 2250 and a mobileindustry processor interface (MIPI) display interface 2255. Storage maybe provided by a flash memory subsystem 2260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 2265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine2270.

FIG. 23 is a block diagram illustrating an exemplary graphics processor2310 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 2310 can be a variant of the graphics processor 2210 of FIG.22. Graphics processor 2310 includes a vertex processor 2305 and one ormore fragment processor(s) 2315A-2315N (e.g., 2315A, 2315B, 2315C,2315D, through 2315N-1, and 2315N). Graphics processor 2310 can executedifferent shader programs via separate logic, such that the vertexprocessor 2305 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 2315A-2315Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 2305 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 2315A-2315N use the primitiveand vertex data generated by the vertex processor 2305 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 2315A-2315N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 2310 additionally includes one or more memorymanagement units (MMUs) 2320A-2320B, cache(s) 2325A-2325B, and circuitinterconnect(s) 2330A-2330B. The one or more MMU(s) 2320A-2320B providefor virtual to physical address mapping for integrated circuit 2310,including for the vertex processor 2305 and/or fragment processor(s)2315A-2315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 2325A-2325B. In one embodiment the one or more MMU(s)2325A-2325B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 2205, image processor 2215, and/or video processor 2220 ofFIG. 22, such that each processor 2205-2220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 2330A-2330B enable graphics processor 2310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 24 is a block diagram illustrating an additional exemplary graphicsprocessor 2410 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 2410 can be a variant of the graphics processor 2210of FIG. 22. Graphics processor 2410 includes the one or more MMU(s)2320A-2320B, caches 2325A-2325B, and circuit interconnects 2330A-2330Bof the integrated circuit 2300 of FIG. 23.

Graphics processor 2410 includes one or more shader core(s) 2415A-2415N(e.g., 2415A, 2415B, 2415C, 2415D, 2415E, 2415F, through 2415N-1, and2415N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 2410 includes an inter-core taskmanager 2405, which acts as a thread dispatcher to dispatch executionthreads to one or more shader cores 2415A-2415N and a tiling unit 2418to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

The following pertains to further examples.

Example 1 may optionally include an apparatus comprising logic, at leastpartially comprising hardware logic, to receive one or more frames for aworkload; determine one or more compute resource parameters for theworkload; and store the one or more compute resource parameters for theworkload in a memory in association with workload context data for theworkload.

Example 2 may optionally include the subject matter of example 1,wherein the compute resource parameters comprise at least one of anoperating status of one or more execution units assigned to theworkload; and a power status of one or more execution units assigned tothe workload.

Example 3 may optionally include the subject matter of any one ofexamples 1-2, further comprising logic, at least partially includinghardware logic, to receive an additional one or more frames for theworkload; retrieve the one or more compute resource parameters from thememory; and use the compute resource parameters to schedule computeresources to process the additional one or more frames for the workload.

Example 4 may optionally include the subject matter of any one ofexamples 1-3, further comprising logic, at least partially includinghardware logic, to update the compute resource parameters in memory.

Example 5 may optionally include the subject matter of any one ofexamples 1-4, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminedtime frame.

Example 6 may optionally include the subject matter of any one ofexamples 1-5, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminednumber of workload cycles.

Example 7 may optionally include the subject matter of any one ofexamples 1-6, further comprising logic, at least partially includinghardware logic, to delete the compute resource parameters in memory whenthe workload is finished.

Example 8 is an electronic device, comprising a processor having one ormore processor cores, logic, at least partially comprising hardwarelogic, to: receive one or more frames for a workload; determine one ormore compute resource parameters for the workload; and store the one ormore compute resource parameters for the workload in a memory inassociation with workload context data for the workload.

Example 9 may optionally include the subject matter of example 8,wherein the compute resource parameters comprise at least one of anoperating status of one or more execution units assigned to theworkload; and a power status of one or more execution units assigned tothe workload.

Example 10 may optionally include the subject matter of any one ofexamples 8-9, further comprising logic, at least partially includinghardware logic, to receive an additional one or more frames for theworkload; retrieve the one or more compute resource parameters from thememory; and use the compute resource parameters to schedule computeresources to process the additional one or more frames for the workload.

Example 11 may optionally include the subject matter of any one ofexamples 8-10, further comprising logic, at least partially includinghardware logic, to update the compute resource parameters in memory.

Example 12 may optionally include the subject matter of any one ofexamples 8-11, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminedtime frame.

Example 13 may optionally include the subject matter of any one ofexamples 8-12, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminednumber of workload cycles.

Example 14 may optionally include the subject matter of any one ofexamples 8-13, further comprising logic, at least partially includinghardware logic, to delete the compute resource parameters in memory whenthe workload is finished.

Example 15 is a method comprising receiving, in a processing device, oneor more frames for a workload; determining one or more compute resourceparameters for the workload; and storing the one or more computeresource parameters for the workload in a memory in association withworkload context data for the workload.

Example 16 may optionally include the subject matter of example 15,wherein the compute resource parameters comprise at least one of anoperating status of one or more execution units assigned to theworkload; and a power status of one or more execution units assigned tothe workload.

Example 17 may optionally include the subject matter of any one ofexamples 15-16, further comprising receiving an additional one or moreframes for the workload; retrieving the one or more compute resourceparameters from the memory; and using the compute resource parameters toschedule compute resources to process the additional one or more framesfor the workload.

Example 18 may optionally include the subject matter of any one ofexamples 15-17, further comprising updating the compute resourceparameters in memory.

Example 19 may optionally include the subject matter of any one ofexamples 15-18, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminedtime frame.

Example 20 may optionally include the subject matter of any one ofexamples 15-19, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminednumber of workload cycles.

Example 21 may optionally include the subject matter of any one ofexamples 15-20, further comprising deleting the compute resourceparameters in memory when the workload is finished.

Example 22 is one or more computer-readable medium comprising one ormore instructions that when executed on at least one processor configurethe at least one processor to perform one or more operations to receiveone or more frames for a workload; determine one or more computeresource parameters for the workload; and store the one or more computeresource parameters for the workload in a memory in association withworkload context data for the workload.

Example 23 may optionally include the subject matter of example 22,wherein the compute resource parameters comprise at least one of anoperating status of one or more execution units assigned to theworkload; and a power status of one or more execution units assigned tothe workload.

Example 24 may optionally include the subject matter of any one ofexamples 22-23, further comprising one or more instructions that whenexecuted on the at least one processor configure the at least oneprocessor to receive an additional one or more frames for the workload;retrieve the one or more compute resource parameters from the memory;and use the compute resource parameters to schedule compute resources toprocess the additional one or more frames for the workload.

Example 25 may optionally include the subject matter of any one ofexamples 22-24, further comprising logic, at least partially includinghardware logic, to update the compute resource parameters in memory.

Example 26 may optionally include the subject matter of any one ofexamples 22-25, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminedtime frame.

Example 27 may optionally include the subject matter of any one ofexamples 22-26, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminednumber of workload cycles.

Example 28 may optionally include the subject matter of any one ofexamples 22-27, further comprising logic, at least partially includinghardware logic, to delete the compute resource parameters in memory whenthe workload is finished.

In various embodiments, the operations discussed herein may beimplemented as hardware (e.g., logic circuitry), software, firmware, orcombinations thereof, which may be provided as a computer programproduct, e.g., including a tangible (e.g., non-transitory)machine-readable or computer-readable medium having stored thereoninstructions (or software procedures) used to program a computer toperform a process discussed herein. The machine-readable medium mayinclude an information storage device.

Additionally, such computer-readable media may be downloaded as acomputer program product, wherein the program may be transferred from aremote computer (e.g., a server) to a requesting computer (e.g., aclient) by way of data signals provided in a carrier wave or otherpropagation medium via a communication link (e.g., a bus, a modem, or anetwork connection).

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, and/or characteristicdescribed in connection with the embodiment may be included in at leastan implementation. The appearances of the phrase “in one embodiment” invarious places in the specification may or may not be all referring tothe same embodiment.

Also, in the description and claims, the terms “coupled” and“connected,” along with their derivatives, may be used. In someembodiments, “connected” may be used to indicate that two or moreelements are in direct physical or electrical contact with each other.“Coupled” may mean that two or more elements are in direct physical orelectrical contact. However, “coupled” may also mean that two or moreelements may not be in direct contact with each other, but may stillcooperate or interact with each other.

Thus, although embodiments have been described in language specific tostructural features and/or methodological acts, it is to be understoodthat claimed subject matter may not be limited to the specific featuresor acts described. Rather, the specific features and acts are disclosedas sample forms of implementing the claimed subject matter.

The invention claimed is:
 1. An apparatus comprising: a threadscheduler; a general purpose graphics processing unit comprising aplurality of streaming multiprocessors (SMs) communicatively coupled tothe thread scheduler and comprising a plurality of execution units and alocal cache memory, the graphics processing unit to: receive one or moreframes for a workload; determine one or more compute resource parametersof the plurality of streaming microprocessors for the workload; andstore the one or more compute resource parameters for the workload in athe local cache memory of at least one of the plurality of streamingmicroprocessors in association with workload context data for theworkload.
 2. The apparatus of claim 1, wherein the compute resourceparameters comprise at least one of: an operating status of one or moreexecution units assigned to the workload; and a power status of one ormore execution units assigned to the workload.
 3. The apparatus of claim2, the graphics processing unit to: receive an additional one or moreframes for the workload; retrieve the one or more compute resourceparameters from the local cache memory; and use the compute resourceparameters to schedule compute resources to process the additional oneor more frames for the workload.
 4. The apparatus of claim 3, thegraphics processing unit to: update the compute resource parameters inthe local cache memory.
 5. The apparatus of claim 4, wherein the computeresource parameters define a compute resource parameter usage profilefor a workload over a predetermined time frame.
 6. The apparatus ofclaim 4, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminednumber of workload cycles.
 7. The apparatus of claim 6, the graphicsprocessing unit to: delete the compute resource parameters in the localcache memory when the workload is finished.
 8. An electronic device,comprising: a thread scheduler; a general purpose graphics processingunit comprising a plurality of streaming multiprocessors (SMs)communicatively coupled to the thread scheduler and comprising aplurality of execution units and a local cache memory, the graphicsprocessing unit to: receive one or more frames for a workload; determineone or more compute resource parameters of the plurality of streamingmicroprocessors for the workload; and store the one or more computeresource parameters for the workload in a the local cache memory of atleast one of the plurality of streaming microprocessors in associationwith workload context data for the workload; and a display devicecommunicatively coupled to the processor.
 9. The electronic device ofclaim 8, wherein the compute resource parameters comprise at least oneof: an operating status of one or more execution units assigned to theworkload; a power status of one or more execution units assigned to theworkload.
 10. The electronic device of claim 9, the processor to:receive an additional one or more frames for the workload; retrieve theone or more compute resource parameters from the local cache memory; anduse the compute resource parameters to schedule compute resources toprocess the additional one or more frames for the workload.
 11. Theelectronic device of claim 10, the processor to: update the computeresource parameters in memory.
 12. The electronic device of claim 11,wherein the compute resource parameters define a compute resourceparameter usage profile for a workload over a predetermined time frame.13. The electronic device of claim 11, wherein the compute resourceparameters define a compute resource parameter usage profile for aworkload over a predetermined number of workload cycles.
 14. Theelectronic device of claim 11, the processor to: delete the computeresource parameters in the local cache memory when the workload isfinished.
 15. A method comprising: receiving, in a general purposegraphics processing unit comprising a plurality of streamingmultiprocessors (SMs) communicatively coupled to a thread scheduler andcomprising a plurality of execution units and a local cache memory, oneor more frames for a workload; determining one or more compute resourceparameters of the plurality of streaming microprocessors for theworkload; and storing the one or more compute resource parameters forthe workload in a the local cache memory of at least one of theplurality of streaming microprocessors in association with workloadcontext data for the workload.
 16. The method of claim 15, wherein thecompute resource parameters comprise at least one of: an operatingstatus of one or more execution units assigned to the workload; a powerstatus of one or more execution units assigned to the workload.
 17. Themethod of claim 16, further comprising: receiving an additional one ormore frames for the workload; retrieving the one or more computeresource parameters from the local cache memory; and using the computeresource parameters to schedule compute resources to process theadditional one or more frames for the workload.
 18. The method of claim17, further comprising: update the compute resource parameters in thelocal cache memory.
 19. The method of claim 18, wherein the computeresource parameters define a compute resource parameter usage profilefor a workload over a predetermined time frame.
 20. The method of claim18, wherein the compute resource parameters define a compute resourceparameter usage profile for a workload over a predetermined number ofworkload cycles.
 21. The method of claim 18, further comprising:deleting the compute resource parameters in the local cache memory whenthe workload is finished.
 22. One or more non-transitorycomputer-readable medium comprising one or more instructions that whenexecuted on at least one processor configure the at least one processorto perform one or more operations to: receive, in a general purposegraphics processing unit comprising a plurality of streamingmultiprocessors (SMs) communicatively coupled to a thread schedulercomprising a plurality of execution units and a local cache memory, oneor more frames for a workload; determine one or more compute resourceparameters of the plurality of streaming microprocessors for theworkload; and store the one or more compute resource parameters for theworkload in a the local cache memory of at least one of the plurality ofstreaming microprocessors in association with workload context data forthe workload.
 23. The non-transitory computer-readable medium of claim22 wherein the compute resource parameters comprise at least one of: anoperating status of one or more execution units assigned to theworkload; a power status of one or more execution units assigned to theworkload.
 24. The non-transitory computer-readable medium of claim 23,comprising one or more instructions that when executed on the at leastone processor configure the at least one processor to: receive anadditional one or more frames for the workload; retrieve the one or morecompute resource parameters from the local cache memory; and use thecompute resource parameters to schedule compute resources to process theadditional one or more frames for the workload.
 25. The non-transitorycomputer-readable medium of claim 24, comprising one or moreinstructions that when executed on the at least one processor configurethe at least one processor to: update the compute resource parameters inthe local cache memory.
 26. The non-transitory computer-readable mediumof claim 25, wherein the compute resource parameters define a computeresource parameter usage profile for a workload over a predeterminedtime frame.
 27. The non-transitory computer-readable medium of claim 25,wherein the compute resource parameters define a compute resourceparameter usage profile for a workload over a predetermined number ofworkload cycles.
 28. The non-transitory computer-readable medium ofclaim 25, comprising one or more instructions that when executed on theat least one processor configure the at least one processor to: deletethe compute resource parameters in the local cache memory when theworkload is finished.